Week 6
#install.packages('Seurat')
library(Seurat)
library(SeuratData)
library(ggplot2)
library(patchwork)
library(dplyr)
setwd("~/Desktop/compbio/week6")scRNA data
load scRNA data
pbmc.data <- Read10X(data.dir = "filtered_gene_bc_matrices/hg19/")
pbmc <- CreateSeuratObject(counts = pbmc.data, project = "pbmc3k", min.cells = 3, min.features = 200)Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')pbmcAn object of class Seurat
13714 features across 2700 samples within 1 assay
Active assay: RNA (13714 features, 0 variable features)pre-processing
pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-")
VlnPlot(pbmc, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
plot1 <- FeatureScatter(pbmc, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(pbmc, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
nomarlization
pbmc <- NormalizeData(pbmc)Performing log-normalization
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|identification of highly variable features
pbmc <- FindVariableFeatures(pbmc, selection.method = "vst", nfeatures = 2000)Calculating gene variances
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**************************************************|
Calculating feature variances of standardized and clipped values
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**************************************************|top10 <- head(VariableFeatures(pbmc), 10)
plot1 <- VariableFeaturePlot(pbmc)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)When using repel, set xnudge and ynudge to 0 for optimal resultsplot1 + plot2
scaling
all.genes <- rownames(pbmc)
pbmc <- ScaleData(pbmc, features = all.genes)Centering and scaling data matrix
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|====================================================================================================| 100%dimensionality reduction (PCA, UMAP) and culstering
PCA
pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc))PC_ 1
Positive: MALAT1, LTB, IL32, CD2, ACAP1, STK17A, CTSW, CD247, CCL5, GIMAP5
AQP3, GZMA, CST7, TRAF3IP3, MAL, HOPX, ITM2A, GZMK, MYC, BEX2
GIMAP7, ETS1, LDLRAP1, ZAP70, LYAR, RIC3, TNFAIP8, KLRG1, SAMD3, NKG7
Negative: CST3, TYROBP, LST1, AIF1, FTL, FCN1, LYZ, FTH1, S100A9, FCER1G
TYMP, CFD, LGALS1, CTSS, S100A8, SERPINA1, LGALS2, SPI1, IFITM3, PSAP
CFP, SAT1, IFI30, COTL1, S100A11, NPC2, LGALS3, GSTP1, PYCARD, NCF2
PC_ 2
Positive: CD79A, MS4A1, TCL1A, HLA-DQA1, HLA-DRA, HLA-DQB1, LINC00926, CD79B, HLA-DRB1, CD74
HLA-DPB1, HLA-DMA, HLA-DQA2, HLA-DRB5, HLA-DPA1, HLA-DMB, FCRLA, HVCN1, LTB, BLNK
KIAA0125, P2RX5, IRF8, IGLL5, SWAP70, ARHGAP24, SMIM14, PPP1R14A, FCRL2, C16orf74
Negative: NKG7, PRF1, CST7, GZMA, GZMB, FGFBP2, CTSW, GNLY, GZMH, SPON2
CCL4, FCGR3A, CCL5, CD247, XCL2, CLIC3, AKR1C3, SRGN, HOPX, CTSC
TTC38, S100A4, ANXA1, IL32, IGFBP7, ID2, ACTB, XCL1, APOBEC3G, SAMD3
PC_ 3
Positive: HLA-DQA1, CD79A, CD79B, HLA-DQB1, HLA-DPB1, CD74, HLA-DPA1, MS4A1, HLA-DRB1, HLA-DRB5
HLA-DRA, HLA-DQA2, TCL1A, LINC00926, HLA-DMB, HLA-DMA, HVCN1, FCRLA, IRF8, BLNK
KIAA0125, SMIM14, PLD4, IGLL5, P2RX5, TMSB10, SWAP70, LAT2, MALAT1, IGJ
Negative: PPBP, PF4, SDPR, SPARC, GNG11, NRGN, GP9, RGS18, TUBB1, CLU
HIST1H2AC, AP001189.4, ITGA2B, CD9, TMEM40, CA2, PTCRA, ACRBP, MMD, TREML1
NGFRAP1, F13A1, RUFY1, SEPT5, MPP1, CMTM5, TSC22D1, MYL9, RP11-367G6.3, GP1BA
PC_ 4
Positive: HLA-DQA1, CD79A, CD79B, HIST1H2AC, HLA-DQB1, PF4, MS4A1, SDPR, CD74, PPBP
HLA-DPB1, GNG11, HLA-DQA2, SPARC, HLA-DRB1, HLA-DPA1, GP9, TCL1A, HLA-DRA, LINC00926
NRGN, RGS18, HLA-DRB5, PTCRA, CD9, AP001189.4, CA2, CLU, TUBB1, ITGA2B
Negative: VIM, S100A8, S100A6, S100A4, S100A9, TMSB10, IL32, GIMAP7, LGALS2, S100A10
RBP7, FCN1, MAL, LYZ, S100A12, MS4A6A, CD2, FYB, S100A11, FOLR3
GIMAP4, AQP3, ANXA1, AIF1, MALAT1, GIMAP5, IL8, IFI6, TRABD2A, TMSB4X
PC_ 5
Positive: GZMB, FGFBP2, NKG7, GNLY, PRF1, CCL4, CST7, SPON2, GZMA, CLIC3
GZMH, XCL2, CTSW, TTC38, AKR1C3, CCL5, IGFBP7, XCL1, CCL3, S100A8
TYROBP, HOPX, CD160, HAVCR2, S100A9, FCER1G, PTGDR, LGALS2, RBP7, S100A12
Negative: LTB, VIM, AQP3, PPA1, MAL, KIAA0101, CD2, CYTIP, CORO1B, FYB
IL32, TRADD, ANXA5, TUBA1B, HN1, TYMS, PTGES3, ITM2A, COTL1, GPR183
TNFAIP8, ACTG1, TRAF3IP3, ATP5C1, GIMAP4, ZWINT, PRDX1, LDLRAP1, ABRACL, NGFRAP1 print(pbmc[["pca"]], dims = 1:5, nfeatures = 5)PC_ 1
Positive: MALAT1, LTB, IL32, CD2, ACAP1
Negative: CST3, TYROBP, LST1, AIF1, FTL
PC_ 2
Positive: CD79A, MS4A1, TCL1A, HLA-DQA1, HLA-DRA
Negative: NKG7, PRF1, CST7, GZMA, GZMB
PC_ 3
Positive: HLA-DQA1, CD79A, CD79B, HLA-DQB1, HLA-DPB1
Negative: PPBP, PF4, SDPR, SPARC, GNG11
PC_ 4
Positive: HLA-DQA1, CD79A, CD79B, HIST1H2AC, HLA-DQB1
Negative: VIM, S100A8, S100A6, S100A4, S100A9
PC_ 5
Positive: GZMB, FGFBP2, NKG7, GNLY, PRF1
Negative: LTB, VIM, AQP3, PPA1, MAL VizDimLoadings(pbmc, dims = 1:2, reduction = "pca")
DimPlot(pbmc, reduction = "pca")
clustering
pbmc <- FindNeighbors(pbmc, dims = 1:10)Computing nearest neighbor graph
Computing SNNpbmc <- FindClusters(pbmc, resolution = 0.5)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 2700
Number of edges: 97892
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8719
Number of communities: 9
Elapsed time: 0 secondshead(Idents(pbmc), 5)AAACATACAACCAC-1 AAACATTGAGCTAC-1 AAACATTGATCAGC-1 AAACCGTGCTTCCG-1 AAACCGTGTATGCG-1
0 3 2 5 6
Levels: 0 1 2 3 4 5 6 7 8UMAP
pbmc <- RunUMAP(pbmc, dims = 1:10)21:11:10 UMAP embedding parameters a = 0.9922 b = 1.112
21:11:10 Read 2700 rows and found 10 numeric columns
21:11:10 Using Annoy for neighbor search, n_neighbors = 30
21:11:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:11:10 Writing NN index file to temp file /var/folders/41/wxyyz25j4m324br78sjcqf5w0000gn/T//RtmptGf36u/file199249edcd0f
21:11:10 Searching Annoy index using 1 thread, search_k = 3000
21:11:11 Annoy recall = 100%
21:11:11 Commencing smooth kNN distance calibration using 1 thread
21:11:12 Initializing from normalized Laplacian + noise
21:11:12 Commencing optimization for 500 epochs, with 107868 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:11:15 Optimization finishedDimPlot(pbmc, reduction = "umap")
biomarker
cluster2.markers <- FindMarkers(pbmc, ident.1 = 2, min.pct = 0.25)
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s head(cluster2.markers, n = 5)cluster5.markers <- FindMarkers(pbmc, ident.1 = 5, ident.2 = c(0, 3), min.pct = 0.25)
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# InstallData("stxBrain")
brain <- LoadData("stxBrain", type = "anterior1")Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be used
Warning in if (is.na(desc)) { :
the condition has length > 1 and only the first element will be usedplot1 <- VlnPlot(brain, features = "nCount_Spatial", pt.size = 0.1) + NoLegend()
plot2 <- SpatialFeaturePlot(brain, features = "nCount_Spatial") + theme(legend.position = "right")Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.wrap_plots(plot1, plot2)
brain <- SCTransform(brain, assay = "Spatial", verbose = FALSE)gene expression visualization
SpatialFeaturePlot(brain, features = c("Hpca", "Ttr"))Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
p1 <- SpatialFeaturePlot(brain, features = "Ttr", pt.size.factor = 1)Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.p2 <- SpatialFeaturePlot(brain, features = "Ttr", alpha = c(0.1, 1))Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.p1 + p2
dimensionality reduction and culstering
brain <- RunPCA(brain, assay = "SCT", verbose = FALSE)
brain <- FindNeighbors(brain, reduction = "pca", dims = 1:30)Computing nearest neighbor graph
Computing SNNbrain <- FindClusters(brain, verbose = FALSE)
brain <- RunUMAP(brain, reduction = "pca", dims = 1:30)21:14:16 UMAP embedding parameters a = 0.9922 b = 1.112
21:14:16 Read 2696 rows and found 30 numeric columns
21:14:16 Using Annoy for neighbor search, n_neighbors = 30
21:14:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:14:17 Writing NN index file to temp file /var/folders/41/wxyyz25j4m324br78sjcqf5w0000gn/T//RtmptGf36u/file19926357a672
21:14:17 Searching Annoy index using 1 thread, search_k = 3000
21:14:17 Annoy recall = 100%
21:14:17 Commencing smooth kNN distance calibration using 1 thread
21:14:18 Initializing from normalized Laplacian + noise
21:14:18 Commencing optimization for 500 epochs, with 105696 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:14:22 Optimization finishedp1 <- DimPlot(brain, reduction = "umap", label = TRUE)p2 <- SpatialDimPlot(brain, label = TRUE, label.size = 3)Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing
scale.p1 + p2
SpatialDimPlot(brain, cells.highlight = CellsByIdentities(object = brain, idents = c(2, 1, 4, 3, 5, 8)),
facet.highlight = TRUE, ncol = 3)
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